2008
DOI: 10.1002/spe.915
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Scopira: an open source C++ framework for biomedical data analysis applications

Abstract: In many biomedical research laboratories, data analysis and visualization algorithms are typical prototypes using an interpreted programming language. If performance becomes an issue, they are ported to C and integrated with interpreted systems, not fully utilizing object-oriented software development. This paper presents an overview of Scopira, an open source C++ framework suitable for biomedical data analysis and visualization. Scopira provides high-performance end-to-end application development features, in… Show more

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Cited by 9 publications
(10 citation statements)
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“…ROI erosion used a 3 by 3 structuring element. Implementation was in C++ code as a ScopiraTm kit [10]. [9][10][11][12] show ROI for correlation thresholds.…”
Section: A Fmri Spatio-temporal Methodsmentioning
confidence: 99%
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“…ROI erosion used a 3 by 3 structuring element. Implementation was in C++ code as a ScopiraTm kit [10]. [9][10][11][12] show ROI for correlation thresholds.…”
Section: A Fmri Spatio-temporal Methodsmentioning
confidence: 99%
“…Implementation was in C++ code as a ScopiraTm kit [10]. [9][10][11][12] show ROI for correlation thresholds. Initial ROI exhibit irregular structure, roughly corresponding to anatomical structure of the visual cortex, and often contain holes of low average intensity or low correlation.…”
Section: A Fmri Spatio-temporal Methodsmentioning
confidence: 99%
“…This includes the evaluation of software engineering tools and methods (Canfora and Troiano 2002), improving the quality of decision-making in the software development project under uncertain conditions (Büyüközkan et al 2004), identifying software quality requirements (Oliveira et al 1999), software cost estimation (Sicilia et al 2005), assessment of software maintainability (Canfora et al 2004), embedding risk assessment information into software cost estimation (Huang et al 2006), selecting software configuration items (Wang and Lin 2003), and quantifying software complexity (Pedrycz and Sosnowski 2001). Here, the integral (Sugeno 1972) is used to aggregate software component predictions from a set of linear classifiers operating on subsets of software metrics chosen via a stochastic feature selection process (Pizzi 2005).…”
Section: Quality Prediction Using Fuzzy Integrationmentioning
confidence: 99%
“…The probabilities of selecting one of these quadrate feature combination categories must sum to 1. SFS was implemented using Scopira http://scopira.org (2006), an algorithm development framework, which allows the interconnection of multiple algorithm "modules" (for example, classifiers and data preprocessing techniques) (Demko et al 2002(Demko et al , 2005. Algorithm functionality may be mapped onto a computer cluster to better utilize CPU processing power while decreasing overall execution time.…”
Section: Stochastic Feature Selectionmentioning
confidence: 99%
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